A data store is a repository of amorphous data. Generally, amorphous data is data that does not conform to any particular form or structure. Typically, data sourced from several different sources of different types is amorphous because the sources provide the data in varying formats, organized in different ways, and often in unstructured form.
A data cube is a quantum of data that can be sold, purchased, borrowed, installed, loaded, or otherwise used in a computation. Several methods for querying amorphous data from one or more data stores are presently in use. Presently, all the amorphous data that is to be queried is first organized in a data structure with a suitable number of columns to represent all of the amorphous data, e.g., as a large multi-column table data cube, using any known technique for constructing such data structures. A query is then constructed corresponding to the columns represented in the data structure.
Querying amorphous data produces a result set that is also amorphous. A result set is data resulting from executing a query. Executing a portion of a query, or a sub-query, also results in a result set.
Normalization of data is a process of organizing the data. Structuring unstructured data, for example, casting or transforming amorphous data into some structured form, is an example of normalizing amorphous data.